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I'm trying to find a way to screen scrape the letters and numbers (mainly numbers) from the attached picture.
example picture
In previous attempts, I've used pyocr and many other variations.
My question is, has any body found a way to scrape off numbers? Or how to train the pyocr algorithm to use custom data?
Thanks in advance!
The folks at PyImageSearch have a TON of info about processing images in Python with OpenCV.
They even have a free blog post about using Tesseract OCR. Though Tesseract can be a bit fussy about fonts, the good news is it looks like your text in the image should always be the same font, and perfectly aligned horizontally and vertically.
(disclaimer: I'm a student of theirs; but I don't work for them)
https://www.pyimagesearch.com/2018/09/17/opencv-ocr-and-text-recognition-with-tesseract/
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I'm having trouble installing opencv. Is it possible to get contours from a picture without using opencv in python?
You could iterate over the pixels using a for loop, darken each pixel until you’ve got your more prominent values, then create an array of vectors dependant on the outline, tensorflow posenet does this
OpenCV is more widely used for this purpose, but you can also do great things with:
Scikit-image find_contours measure: Check code & sample here
PIL/Pillow: For simpler uses. Check this example
Tensoflow: For more advanced use, there are some approaches too using Deep Learning for this. Just found this sample searching Github
Pytorch: Also via deep learning. Check this other sample
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I want to detect a watermark in an image using OpenCV.
Particularly, I want a rectangular box around the watermark, if present.
Can you please help me out with the python code?
Though the solution would be dependent on the actual image content (that needs to be preserved) and the watermark. But in these kinds of problems, following sequence of steps is usually followed:
Converting the image to grayscale (cv2.cvtColor(img, cv2.COLOR_BGR2GRAY))
Applying morphological filtering Erosion, Dilation
Taking the difference of this output from the actual image
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I want to play a bunch of movies on a grid let's say of dimension (n, n), where each movie m_ij is an element of the grid. I would like to decide which movie is positioned where on the grid at each second, i.e. movies can switch positions over time. How would one start to do this? I'd preferably use Python, but am open to use another language if appropriate. Thanks a lot for any help, it's for an art project.
I know you prefer Python but first think I can think of to achieve something like that easily would be through using this JavaScript library called p5js or processing which is same library written in Java. It is very good for doing art with code kinda thing.
Here is a video to give you the idea.
Anyways hope this helps. Cheers.
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I came across some answers regarding the possibility of image recognition (and further matching) in Python and other languages, as well as papers describing a machine learning process for matching signatures*, but none in R regarding the possibility of recognizing signatures. In Python, a language in which I might try a hand at, I found the OpenCV library for face recognition. But still is not exactly what I need.
For an R user (and someone willing to instruct himself in Python), what is the best strategy -- considering time, nothing that it would take more than a few days of learning -- to create an algorithm for signature matching in R or Python?
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My goal is to recognize specific types of traffic signs: red circles on video in real time.
Scikit-Image and OpenCV are both great for their respective uses. Unfortunately, as far as I know, Scikit-Image doesn't have the ability to do real-time video. OpenCV is much better for this problem.
If you don't know where to begin, here's a great tutorial series. It is exactly what you're asking, just substitute a stop-sign trained classifier for the face trained classifier. https://pythonprogramming.net/haar-cascade-face-eye-detection-python-opencv-tutorial/
Use a Haar cascade for the sign detection.
I found with a quick search for 'Stop sign Haar cascade' that other people already have created trained classifiers like this http://www.cs.utah.edu/~turcsans/DUC_files/HaarCascades/
On the page, download haarcascade_stop.xml
In my opinion OpenCV is better choice for you. You can find many tutorial for your goal. Here is a simple tutorial for beginners;
http://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_tutorials.html